BioDT - The Recreational Model

1. Scope of the Model

The recreational potential model (RP Model) was developed as part of the Cultural Ecosystem Services prototype Digital Twin (CES-pDT) workpackage within the BioDT project. The (CES-pDT) has two independent core models:

• the Biodiversity model (developed by Simon Rolph and Dylan Carbone, UK Centre for Ecology & Hydrology, Wallingford, United Kingdom): aims at estimating biodiversity levels across mammals, birds, plant and insects.

• the Recreational Potential model (developed by Jan Dick, Chris Andrews, Maddalena Tigli, Megan Williams and Joe Marsh Rossney, UK Centre for Ecology & Hydrology, Edinburgh, United Kingdom): aims at estimating the landscapes’ capacity to provide opportunities for outdoor recreation based on varying user interests.

This report specifically documents the Recreational Potential (RP) Model, detailing its methodology, data sources, operational mechanisms, and outlining areas for future refinement and development.

2. The model

2.1. Model description

flowchart LR
 subgraph s1["compute recreational potential"]
        C[("cropping of the 4<br>components file to the<br>extent of interest")]
        E["Compute each component's<br>contribution"]
        F["Rescale to unit interval"]
  end
    A("Definition of the<br><i>persona preferences</i>") ---> E
    B("Definition of the<br><i>area of interest</i>") --> C
    C --> E
    E --> F
    F --> G["recreational potential spatRast"]
    style s1 fill:#FFF9C4
    style A color:#000000
    style B color:#000000
    style C color:#000000
    style E color:#000000
    style F color:#000000
    style G color:#000000

The RP Model uses of 4 key components, each representing a different aspect of a landscape’s recreational value:

1) the landscape component (SLSRA)

2) the natural features component (FIPS_N)

3) the infrastructure component (FIPS_I)

4) the water component (Water)

For each of the 4 components, a corresponding raster file was generated ( see [3. The underlying data] paragraph to see where they came from), covering the entirety of Scotland, which is the domain that the RP model covers at the moment. Each component’s raster file store several layers, each describing a specific aspect of that component. The four raster files form the basis for calculating the recreational potential of an area, which is the main output of the RP Model.

In order to run the RP model the user must have made 2 decisions:

• the area of interest

• the persona preferences

Using the 4 components raster files, the recreational potential is calculated by (1) cropping the files to the area of interest, (2) compute each components contribution, (3) re scaling to unit interval.

area of interest

defined with a bounding box generated with the terra::ext() function

persona preferences

a csv with a numeric score from 0 to 10 for each of the 87 features (one feature = one layer): 13 features for the water component, 40 features for the landscape component, 24 features for the natural component and 10 features for the infrastructure component. The csv has in the first column, the name of the features, and in the second one the name and score of each persona. (see example persona file “presets.csv”). Where a higher score means a higher interest for that specific feature.

cropping the files to the area of interest

The first action the model completes is to crop the 4 components rasters to the area of interest and it does so by using the terra package in R. The terra::vect() function is used to convert the extent object provided by the user (“area of interest”) into a vector polygon (i.e., a rectangular polygon bounding box), and then the terra::crop() function to reduce the size of the raster to that of the vector polygon, trimming the rasters into the desired area of interest.

compute each component’s contribution

For each of the 4 components rasters, the model extracts the values of all its layers for each cell (20m x 20m) and multiplies them (if not NAs) with the corresponding score provided by the user (“persona preference”), the, it sums the values of that cell throughout the layers. In other words, the model computes a weighted sum of the raster layers at each cell, returning a new single layer raster with the result.

rescale to unit interval

Each of the components contribution rasters is then re-scaled so that its values are between 0 and 1 using a “min-max normalization”:

\[ scaled.value = (x-min)/mix-min \]

So the minimum values get assigned a 0, the maximum values get assigned a 1 and all other values are proportionally in between.

Them the 4 components re-scaled rasters are summed (each cell’s value is summed with its respective cell of the other components) and the final raster re-scaled once again using the “min-max normalization”.

recreational potential spatRast

The model’s output is a Spat raster with 5 layers. The first 4 layers show the scores for each component: SLSRA, FIPS_N, FIPS_I, Water, and the 5th layer is that of the combined Recreational_Potential.

2.1. Example run

Here we show examples of model output for 2 different and “opposite” personas (a hard recreationalist and a soft recreationalist) fort the Bush area. From the maps it is possible to observe how the model estimates different areas of higher recreational potential for the two personas. Moreover, it is possible to observe how each of the components contributes to the RP.

Hard recreationalist

Here we show a model run the Bush Estate area using a “hard recreationalist” example of persona (saved as “Hard_Recreationalist” in the persona presets in the model’s package). In the tables below it is possible to see which features had the highest and lowest score for this persona.

Features scored the highest and the lowest in the hard recreationalist persona

score_group	features
highest scores (scored 10 or 9)	Rock Walls (FIPS_N), Mountains (FIPS_N), Inland cliffs, rock pavements and outcrops (SLSRA), Rock cliffs, ledges and shores (SLSRA), National Park (SLSRA)
lowest scores (scored 0 or 1)	Built-up areas (FIPS_N), Flood plain (FIPS_N), Depressions (FIPS_N), No slope (FIPS_N), Gentle slope (FIPS_N), Country Park (SLSRA), Raised and blanket bog (SLSRA), Valley mires, poor fens and transition mires (SLSRA), Windthrown woodland (SLSRA), Woodland fringes and clearings and tall forb stands (SLSRA), Bare field or exposed soil (SLSRA), Built-up area (SLSRA), Royal Society for the Protection of Birds (RSPB) Reserve (SLSRA), Pond (Water), Motorway (FIPS_I), A Road (FIPS_I), B Road (FIPS_I), Minor or local road (FIPS_I), Access roads or Track (FIPS_I), Saltings (FIPS_N)

The hard recreationalist for this example highly values rock walls, mountains, inland and rock cliffs, national parks and traffic free areas, whilst are of less interest features like built-up areas, motorways etc.

Soft recreationalist

Here we show a model run the Bush Estate area using a “soft recreationalist” example of persona (saved as “Soft_Recreationalist” in the persona presets in the model’s package). In the tables below it is possible to see which features had the highest and lowest score for this persona.

The soft recreationalist for this example highly values traffic free areas, coastal features (beaches, dunes), lochs and national parks, whilst are of less interest features like rock walls, buil-up areas, steep slopes etc.

Loading persona 'Soft_Recreationalist' from file 'C:/Users/madtig/AppData/Local/R/win-library/4.4/biodt.recreation/extdata/personas/presets.csv'

Selected an area of 3.1e+07 m^2 .

2.3. The underlying data

The methodology used to create each of the 4 components’ raster file is described in detail in this section.

Description of the 4 components’ raster files.

name raster	description
SLSRA.tif	Landscape component This includes data on land cover type, landscape designations and conservation, and farmland of high nature value. resolution: 20x20 extent: whole of Scotland crs: British National Grid nr. features: 40 Contains values that either NAs or 1. NAs if that particular layers’ feature is NOT present in the cell, and 1 if it is.
FIPS_N.tif	Natural Features component This includes data on land form types, soil types and slope. resolution: 20x20 extent: whole of Scotland crs: British National Grid nr. features: 24 Contains values that either NAs or 1. NAs if that particular layers’ feature is NOT present in the cell, and 1 if it is.
Water.tif	Water component This includes data on water feature types, as the presence of a lake or river. resolution: 20x20 extent: whole of Scotland crs: British National Grid nr. features: 13
FIPS_I.tif	Infrastructure component This includes data on road and track, footpaths and cycle networks. resolution: 20x20 extent: whole of Scotland crs: British National Grid nr. features: 10

Landscape component

Landscape component features

nr	Name	Description
1	SLSRA_CP_2	Country Park
2	SLSRA_HNV_2	Designated High Nature Value (HNV) farmland
3	SLSRA_LCM_1	Alpine and subalpine grassland
4	SLSRA_LCM_2	Arable land and market gardens
5	SLSRA_LCM_3	Arctic, alpine and subalpine scrub
6	SLSRA_LCM_4	Bare field or exposed soil
7	SLSRA_LCM_5	Base-rich fens and calcareous spring mires
8	SLSRA_LCM_6	Broadleaved deciduous woodland
9	SLSRA_LCM_7	Built-up area
10	SLSRA_LCM_8	Coastal dunes and sandy shore
11	SLSRA_LCM_9	Coastal shingle
12	SLSRA_LCM_10	Dry grassland
13	SLSRA_LCM_11	Freshwater
14	SLSRA_LCM_12	Inland cliffs, rock pavements and outcrops
15	SLSRA_LCM_13	Lines of trees, small planted woodlands, early-stage woodland and coppice
16	SLSRA_LCM_14	Littoral sediment or saltmarsh
17	SLSRA_LCM_15	Mesic grassland
18	SLSRA_LCM_16	Mixed deciduous and coniferous woodland
19	SLSRA_LCM_17	Non-native coniferous plantation
20	SLSRA_LCM_18	Raised and blanket bog
21	SLSRA_LCM_19	Riverine and fen scrubs
22	SLSRA_LCM_20	Rock cliffs, ledges and shores
23	SLSRA_LCM_21	Scots pine woodland
24	SLSRA_LCM_22	Screes
25	SLSRA_LCM_23	Seasonally wet and wet grassland
26	SLSRA_LCM_24	Temperate montane scrub
27	SLSRA_LCM_25	Temperate shrub heathland
28	SLSRA_LCM_26	Valley mires, poor fens and transition mires
29	SLSRA_LCM_27	Windthrown woodland
30	SLSRA_LCM_28	Woodland fringes and clearings and tall forb stands
31	SLSRA_NNR_2	National Nature Reserve (NNR)
32	SLSRA_NP_2	National Park
33	SLSRA_NR_2	Nature Reserve
34	SLSRA_RP_2	Regional Park
35	SLSRA_RSPB_2	Royal Society for the Protection of Birds (RSPB) Reserve
36	SLSRA_SAC_2	Special Area of Conservation (SAC)
37	SLSRA_SPA_2	Special Protection Area (SPA)
38	SLSRA_SSSI_2	Site of Special Scientific Interest (SSSI)
39	SLSRA_SWT_2	Scottish Wildlife Trust Reserve
40	SLSRA_WLA_2	Wild Land Areas

Natural Features component

Natural Features component features

nr	Name	Description
1	FIPS_N_Landform_1	Foothills
2	FIPS_N_Landform_2	Mountains
3	FIPS_N_Landform_3	Terraces
4	FIPS_N_Landform_4	Flood plain
5	FIPS_N_Landform_5	Beaches or Dunes
6	FIPS_N_Landform_6	Rocks or Scree
7	FIPS_N_Landform_7	Depressions
8	FIPS_N_Landform_8	Hills
9	FIPS_N_Landform_9	Lowlands
10	FIPS_N_Landform_10	Rock Walls
11	FIPS_N_Landform_11	Uplands
12	FIPS_N_Landform_12	Valley sides
13	FIPS_N_Landform_13	Valley bottom
14	FIPS_N_Landform_14	Built-up areas
15	FIPS_N_Landform_15	Saltings
16	FIPS_N_Landform_16	Hummocks, mounds or moraines
17	FIPS_N_Slope_1	No slope
18	FIPS_N_Slope_2	Gentle slope
19	FIPS_N_Slope_3	Medium slope
20	FIPS_N_Slope_4	Steep slope
21	FIPS_N_Slope_5	Very steep slope
22	FIPS_N_Slope_6	Extremely steep slope
23	FIPS_N_Soil_1	Peat or Organic
24	FIPS_N_Soil_2	Mineral

Infrastructure component

Infrastructure component features

nr	Name	Description
1	FIPS_I_LocalPathNetwork_2	Path
2	FIPS_I_RoadsTracks_4	Minor or local road
3	FIPS_I_RoadsTracks_2	A Road
4	FIPS_I_RoadsTracks_1	Motorway
5	FIPS_I_RoadsTracks_5	Access roads or Track
6	FIPS_I_RoadsTracks_3	B Road
7	FIPS_I_NationalCycleNetwork_1	On Road: Paved Surface
8	FIPS_I_NationalCycleNetwork_2	Traffic Free: Unpaved Surface
9	FIPS_I_NationalCycleNetwork_3	Traffic Free: Paved Surface
10	FIPS_I_NationalCycleNetwork_4	On Road: Unpaved Surface

The infrastructure component is derived from a raster with weather or not the cell had or not that feature (1 = it had it; and NA= did not have it). - which is labelled “original” in the example map for Easter Bush; which CHRISTOPHER ANDREWS TO ADD INFO ABOUT IT HERE.

Then, a value = to the the distance in m its center has from the closest value named “distance” in example map, using the terra::distance function. Since this procedure is very memory consuming, the original Scotland wide raster was split into 20 “windows” and for each of them a 10km “buffer” was allowed (to make sure to not overlook the vicinity to features belonging to neighboring windows).

Then, considering only the cells that were <= 500m distance from a feature, the values were re-scaled from 0 to 1 (1 = present in cell, and then decreasing score as you get away from that cell) named “scored” in map below, using the JOE TO ADD PAPER THIS FUNCTION and kappa and alpha values COMES FROM:

\[ distance (m) = \frac{\kappa + 1}{\kappa + \exp(\alpha x)} \]

Water component

Water component features

nr	Name	Description
1	Water_Lakes_1	Pond
2	Water_Lakes_2	Lochan
3	Water_Lakes_3	Small Lochs
4	Water_Lakes_4	Medium Lochs
5	Water_Lakes_5	Large Lochs
6	Water_Lakes_6	Major Lochs
7	Water_Rivers_1	Minor river or tributary
8	Water_Rivers_2	Unnamed minor stream or tributary
9	Water_Rivers_3	Major river or tributary
10	Water_Rivers_4	Named minor stream or tributary
11	Water_Rivers_5	Lake
12	Water_Rivers_6	Tidal river or estuary
13	Water_Rivers_7	Canal

In the same way as infrastructure:

3. The Shiny app for the “watches” spin off

4. Running the model for the whole of Scotland

5. Lessons learned

• using terra:: spatrasts instead of raster:: rasters has improved the performance significantly

• pre-processing the majority of the operations on the scotland wide component rasters (uniforming their extent, resolution, projection, pre calculating the distance, and score etc.) incredibly reduces the run time.

6. Future improvements

• less features o score, merge features into bigger categories

• link the model to “alive” data set” that gets updated regularly to generate up to date original raster layers

• at the moment all four components weight the same, there can be an option to have them weight differently, as the user specifies.